The present invention relates to using computer vision systems, methods or algorithms to search video image data for objects as a function of scene geometry and object motion direction attributes.
Object detection and recognition presents a number of problems in computer vision applications. For example, detecting and distinguishing individuals, vehicles and other objects in video data acquired from views of uncontrolled environments (urban streets, etc.) may be problematic due to inconsistent, poor or variable scene illumination conditions, environments that vary over time (e.g. sunlight, shadows, reflections, rain, snow, night-time street illumination, etc.). The video data may also be acquired from low resolution cameras, and objects may partially occlude each other as they move through a scene relative to a camera viewpoint, particularly in high density situations. Images acquired may also be crowded with multiple objects, comprise fast moving objects, and exhibit high object occurrence and motion frequencies, image clutter, variable object lighting and resolutions within a common scene, as well as distracting competing visual information. All of these situations present a challenge to both human and automated processes for object tracking and recognition in video data.